Despite the recent success achieved by several two-stage prototypical networks in few-shot named entity recognition (NER) task, the over-detected false spans at span detection stage and the inaccurate and unstable prototypes at type classification stage remain to be challenging problems. In this paper, we propose a novel Type-Aware Decomposed framework, namely TadNER, to solve these problems. We first present a type-aware span filtering strategy to filter out false spans by removing those semantically far away from type names. We then present a type-aware contrastive learning strategy to construct more accurate and stable prototypes by jointly exploiting support samples and type names as references. Extensive experiments on various benchmarks prove that our proposed TadNER framework yields a new state-of-the-art performance.

翻译：尽管最近一些两阶段原型网络在少样本命名实体识别任务中取得了成功，但在跨度检测阶段过度检测到的虚假跨度以及类型分类阶段不准确且不稳定的原型仍然具有挑战性。本文提出了一种新颖的类型感知分解框架，即TadNER，以解决这些问题。我们首先提出了一种类型感知的跨度过滤策略，通过移除与类型名称语义距离较远的项来过滤掉虚假跨度。然后，我们提出了一种类型感知的对比学习策略，通过共同利用支持样本和类型名称作为参考，构建更准确且更稳定的原型。在多个基准上进行的大量实验证明，我们提出的TadNER框架取得了新的最优性能。